Results 11 to 20 of about 242,378 (342)
bridgesampling: An R Package for Estimating Normalizing Constants
Journal of Statistical Software, 2020 Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods).
Quentin F. Gronau +2 more
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Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations [PDF]
, 2020 It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary probability theory, of three different mathematical meanings that various ...
de Heide, Rianne +2 more
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Communication in Biomathematical Sciences, 2020 We review basic models of severe/hospitalized and mild/asymptomatic infection spreading (with classes of susceptibles S, hopsitalized H, asymptomatic A and recovered R, hence SHAR-models) and develop the notion of comparing different models on the same ...
Maira Aguiar, Nico Stollenwerk
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Make the most of your samples : Bayes factor estimators for high-dimensional models of sequence evolution [PDF]
, 2013 Background: Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio ...
Baele, Guy +2 more
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Empirical Bayes Matrix Factorization
Journal of machine learning research : JMLR, 2018 Matrix factorization methods - including Factor analysis (FA), and Principal Components Analysis (PCA) - are widely used for inferring and summarizing structure in multivariate data. Many matrix factorization methods exist, corresponding to different assumptions on the elements of the underlying matrix factors.
Wang, Wei, Stephens, Matthew
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The bridge between design and analysis
Perspectives on Medical Education, 2017 The overall purpose of the ‘Statistical Points and Pitfalls’ series is to help readers and researchers alike increase awareness of how to use statistics and why/how we fall into inappropriate choices or interpretations. We hope to help readers understand
Jimmie Leppink +2 more
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Significance, 2006 Abstract Bayes factors are somewhat essential to Bayesian statistics. Tony O'Hagan explains their basics.
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Comparison of Naive Bayes Method and Certainty Factor for Diagnosis of Preeclampsia
Lontar Komputer, 2022 Preeclampsia is a disease often suffered by pregnant women caused by several factors such as a history of heredity, blood pressure, urine protein, and diabetes. The data sample used in this study is data on pregnant women in the 2020 time period recorded
Linda Perdana Wanti +4 more
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Application of Bayesian Analysis in Medical Diagnosis
Journal of the Practice of Cardiovascular Sciences, 2019 In this work, we outlined the application of the Bayesian technique for integrating the results of multiple tests while treating any disease. We provided an overview of the fundamental concept of Bayesian analysis in making an inference about a ...
Vivek Verma +2 more
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From p-Values to Posterior Probabilities of Null Hypotheses
Entropy, 2023 Minimum Bayes factors are commonly used to transform two-sided p-values to lower bounds on the posterior probability of the null hypothesis, in particular the bound −e·p·log(p).
Daiver Vélez Ramos +2 more
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